Lenovo
SKU: 30F3003UUS
Overview
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Overview
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The Lenovo ThinkStation P7 30F3004HUS is a single-socket professional workstation built around the Intel Xeon w5-3435X — a 16-core, 32-thread processor that targets compute-heavy workloads where you need sustained throughput, not just burst speed. With a 3.1 GHz base clock and 4.7 GHz max turbo, combined with a 45 MB Smart Cache and support for up to 1 TB of DDR5 memory, this platform is engineered for workloads that don't fit on a desktop: large-scale VMS server duties, video analytics processing, engineering simulation, or multi-stream 4K rendering environments. It ships with 16 GB of DDR5-SDRAM — enough to get running immediately, with substantial room to scale. For integrators evaluating a professional workstation platform for a compute-intensive surveillance or analytics role, the P7 represents Lenovo's high-end single-socket line.
The Lenovo ThinkStation line integrates with standard enterprise management tooling — the Xeon W platform supports Intel vPro technologies and out-of-band management capabilities typical of Xeon-class systems. The PCIe 4.0/5.0 expansion slots support professional GPU cards from NVIDIA and AMD Radeon Pro lines for accelerated analytics and visualization workloads. For network video recorder or VMS server deployments, pair this platform with a supported NVMe or SATA storage expansion card and a dedicated 10GbE NIC for high-camera-count recording environments. DDR5 memory modules follow the JEDEC DDR5 standard; confirm Lenovo's qualified vendor list (QVL) before sourcing memory upgrades to avoid compatibility issues at scale. For security integrators building out a video surveillance infrastructure that requires a dedicated analytics server, the 16-core Xeon W platform provides the thread count to run deep-learning inference alongside live recording without resource contention. Consider pairing with a managed PoE switch stack if this workstation is serving as a central VMS node receiving feeds from IP cameras across the facility.
Q: What processor does the 30F3004HUS include?
A: The 30F3004HUS ships with the Intel Xeon w5-3435X — a 16-core, 32-thread processor with a 3.1 GHz base clock and 4.7 GHz max turbo frequency, 45 MB Smart Cache, and a 270W base TDP.
Q: How much memory does the 30F3004HUS ship with, and what is the maximum?
A: It ships with 16 GB of DDR5-SDRAM. The platform supports up to 1 TB of maximum installed memory, providing significant headroom for memory-intensive workloads or future expansion.
Q: What PCIe generations are supported in the 30F3004HUS?
A: The ThinkStation P7 30F3004HUS supports both PCIe 4.0 and PCIe 5.0 expansion slots, allowing installation of current-generation GPUs, NVMe storage cards, and high-bandwidth network adapters without a bandwidth bottleneck.
Q: Is the 30F3004HUS a rack-mount or tower workstation?
A: Based on available specifications, this is a tower-class workstation weighing 9 lb. It is not configured as a rack-mount unit. If rack installation is required, verify Lenovo's rack conversion kit compatibility or evaluate the ThinkSystem server line instead.
Q: What is the power draw of the 30F3004HUS?
A: The Intel Xeon w5-3435X has a 270W base processor TDP and up to 324W max turbo power. Total system draw under full load — including GPU, memory, storage, and cooling — will typically exceed 500W. Size UPS and circuit capacity accordingly.

The 30F3004HUS is the configuration I'd reach for when a surveillance deployment has outgrown what a dedicated NVR appliance can handle — specifically when you need to run deep-learning video analytics alongside live multi-stream recording on the same node. The Intel Xeon w5-3435X at 3.1 GHz base and 4.7 GHz turbo across 16 cores gives you the sustained thread count to keep an analytics engine fed without starving the recording process.
Technical Highlights:
Deployment Considerations:
The 30F3004HUS fits best as a centralized VMS analytics server in a mid-to-large enterprise deployment — a corporate campus, hospital, or multi-building retail environment where 50–100+ cameras feed a mix of live recording and AI-driven event detection, and where a dedicated compute node is justified over a shared-resource approach.
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